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Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes

Author

Listed:
  • Christophe Chorro

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Florian Ielpo

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Pictet Asset Management - Pictet Asset Management)

Abstract

This article discusses the finite distance properties of three likelihood-based estimation strategies for GARCH processes with non-Gaussian conditional distributions: (1) the maximum likelihood approach; (2) the Quasi maximum Likelihood approach; (3) a multi-steps recursive estimation approach (REC). We first run a Monte Carlo test which shows that the recursive method may be the most relevant approach for estimation purposes. We then turn to a sample of SP500 returns. We confirm that the REC estimates are statistically dominating the parameters estimated by the two other competing methods. Regardless of the selected model, REC estimates deliver the more stable results.

Suggested Citation

  • Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010. "Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00523371, HAL.
    • Handle: RePEc:hal:cesptp:halshs-00523371
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00523371
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    Cited by:

    1. Guégan, Dominique & Ielpo, Florian & Lalaharison, Hanjarivo, 2013. "Option pricing with discrete time jump processes," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2417-2445.

    More about this item

    Keywords

    Maximum likelihood method; related-GARCH process; recursive estimation method; mixture of Gaussian distribution; Generalized Hyperbolic distributions; SP500; Maximum de vraisemblance; Processus GARCH; méthode d'estimation récursive; mixtures de gaussiennes; distributions hyperbolique Généralisée;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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